---
title: Vector Space Wall
link:
url: https://testimonial.to/qdrant/all
text: Submit Your Testimonial
testimonials:
- id: 0
name: Jonathan Eisenzopf
position: Chief Strategy and Research Officer at Talkmap
avatar:
src: /img/customers/jonathan-eisenzopf.svg
alt: Avatar
text: “With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform on enterprise scale.”
- id: 1
name: Angel Luis Almaraz Sánchez
position: Full Stack | DevOps
avatar:
src: /img/customers/angel-luis-almaraz-sanchez.svg
alt: Avatar
text: Thank you, great work, Qdrant is my favorite option for similarity search.
- id: 2
name: Shubham Krishna
position: ML Engineer @ ML6
avatar:
src: /img/customers/shubham-krishna.svg
alt: Avatar
text: Go ahead and checkout Qdrant. I plan to build a movie retrieval search where you can ask anything regarding a movie based on the vector embeddings generated by a LLM. It can also be used for getting recommendations.
- id: 3
name: Kwok Hing LEON
position: Data Science
avatar:
src: /img/customers/kwok-hing-leon.svg
alt: Avatar
text: Check out qdrant for improving searches. Bye to non-semantic KM engines.
- id: 4
name: Ankur S
position: Building
avatar:
src: /img/customers/ankur-s.svg
alt: Avatar
text: Quadrant is a great vector database. There is a real sense of thought behind the api!
- id: 5
name: Yasin Salimibeni View Yasin Salimibeni’s profile
position: AI Evangelist | Generative AI Product Designer | Entrepreneur | Mentor
avatar:
src: /img/customers/yasin-salimibeni-view-yasin-salimibeni.svg
alt: Avatar
text: Great work. I just started testing Qdrant Azure and I was impressed by the efficiency and speed. Being deploy-ready on large cloud providers is a great plus. Way to go!
- id: 6
name: Marcel Coetzee
position: Data and AI Plumber
avatar:
src: /img/customers/marcel-coetzee.svg
alt: Avatar
text: Using Qdrant as a blazing fact vector store for a stealth project of mine. It offers fantasic functionality for semantic search ✨
- id: 7
name: Andrew Rove
position: Principal Software Engineer
avatar:
src: /img/customers/andrew-rove.svg
alt: Avatar
text: We have been using Qdrant in production now for over 6 months to store vectors for cosine similarity search and it is way more stable and faster than our old ElasticSearch vector index.
No merging segments, no red indexes at random times. It just works and was super easy to deploy via docker to our cluster.
It’s faster, cheaper to host, and more stable, and open source to boot!
- id: 8
name: Josh Lloyd
position: ML Engineer
avatar:
src: /img/customers/josh-lloyd.svg
alt: Avatar
text: I'm using Qdrant to search through thousands of documents to find similar text phrases for question answering. Qdrant's awesome filtering allows me to slice along metadata while I'm at it! 🚀 and it's fast ⏩🔥
- id: 9
name: Leonard Püttmann
position: data scientist
avatar:
src: /img/customers/leonard-puttmann.svg
alt: Avatar
text: Amidst the hype around vector databases, Qdrant is by far my favorite one. It's super fast (written in Rust) and open-source! At Kern AI we use Qdrant for fast document retrieval and to do quick similarity search for text data.
- id: 10
name: Stanislas Polu
position: Software Engineer & Co-Founder, Dust
avatar:
src: /img/customers/stanislas-polu.svg
alt: Avatar
text: Qdrant's the best. By. Far.
- id: 11
name: Sivesh Sukumar
position: Investor at Balderton
avatar:
src: /img/customers/sivesh-sukumar.svg
alt: Avatar
text: We're using Qdrant to help segment and source Europe's next wave of extraordinary companies!
- id: 12
name: Saksham Gupta
position: AI Governance Machine Learning Engineer
avatar:
src: /img/customers/saksham-gupta.svg
alt: Avatar
text: Looking forward to using Qdrant vector similarity search in the clinical trial space! OpenAI Embeddings + Qdrant = Match made in heaven!
- id: 12
name: Rishav Dash
position: Data Scientist
avatar:
src: /img/customers/rishav-dash.svg
alt: Avatar
text: awesome stuff 🔥
sitemapExclude: true
---